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Centrality and Flow Vergence gradient based Path analysis of scientific literature: A case study of Biotechnology for Engineering

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  • Lathabai, Hiran H.
  • Prabhakaran, Thara
  • Changat, Manoj

Abstract

Biotechnology, ever since its inception has had a huge impact on the society and its various applications have been intricately woven into the human web of life. Its evolution amidst all the other research realms vital to mankind is remarkable. In this paper, we intend to identify the radical innovations in Biotechnology for Engineering using network analyses. Centrality analysis and Path analysis are used for identifying important works. Existence of Flow Vergence effect in the scientific literature is revealed. Flow Vergence gradient, an arc metric derived from FV model, is utilised for Path analysis which detects pivotal papers of paradigm shift more accurately. A major paradigm shift has been identified in the business models of Biotechnology for Engineering — ‘Capability to Connectivity’ model. Evidence towards the adoption of business practices in BT firms by nanotechnology start-ups is also identified. The notion of critical divergence is introduced and the exhibition of interdisciplinary interaction in emerging fields due to critical divergence is discussed. Implications of above analyses which target: (i) Science and technology policy makers, (ii) industrialists and investors, (iii) researchers in academia as well as industry, are also discussed.

Suggested Citation

  • Lathabai, Hiran H. & Prabhakaran, Thara & Changat, Manoj, 2015. "Centrality and Flow Vergence gradient based Path analysis of scientific literature: A case study of Biotechnology for Engineering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 157-168.
    • Handle: RePEc:eee:phsmap:v:429:y:2015:i:c:p:157-168
    DOI: 10.1016/j.physa.2015.01.085
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    References listed on IDEAS

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    1. Ismael Rafols & Martin Meyer, 2007. "How cross-disciplinary is bionanotechnology? Explorations in the specialty of molecular motors," Scientometrics, Springer;Akadémiai Kiadó, vol. 70(3), pages 633-650, March.
    2. Prabhakaran, Thara & Lathabai, Hiran H. & Changat, Manoj, 2015. "Detection of paradigm shifts and emerging fields using scientific network: A case study of Information Technology for Engineering," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 124-145.
    3. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    4. Claudia A. Perry & Ronald E. Rice, 1998. "Scholarly communication in developmental dyslexia: Influence of network structure on change in a hybrid problem area," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(2), pages 151-168, February.
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    Cited by:

    1. Thara Prabhakaran & Hiran H. Lathabai & Susan George, 2019. "Competing, complementary and co-existing paradigms in techno-scientific literature: A case study of Nanotechnology for engineering," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 941-977, March.
    2. Hiran H. Lathabai & Susan George & Thara Prabhakaran & Manoj Changat, 2018. "An integrated approach to path analysis for weighted citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1871-1904, December.
    3. Thara Prabhakaran & Hiran H. Lathabai & Susan George & Manoj Changat, 2018. "Towards prediction of paradigm shifts from scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1611-1644, December.
    4. Hiran H. Lathabai & Thara Prabhakaran & Manoj Changat, 2017. "Contextual productivity assessment of authors and journals: a network scientometric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 711-737, February.

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